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Overview

The Generate Embedding operation in this node creates vector embeddings from various types of input content. Embeddings are numerical representations of data (such as text, images, audio, or PDFs) that capture semantic meaning and can be used for tasks like similarity search, clustering, recommendation systems, or machine learning.

Typical use cases include:

  • Converting textual content into embeddings to enable semantic search or document clustering.
  • Generating embeddings from images or audio files for multimedia analysis or classification.
  • Processing URLs or stored files to extract meaningful vector representations without manual content extraction.

For example, you could input a block of text describing a product and generate an embedding to compare it with other products semantically, or provide a PDF file to create embeddings for its content to facilitate search within documents.

Properties

Name Meaning
Embedding Source Choose the source of the content to embed: Text, URL, or File Store Key referencing uploaded files.
Text The raw text content to generate embeddings for (used if Embedding Source is Text).
URL A web address pointing to the resource to generate embeddings for (used if Embedding Source is URL).
File Store Key A key referencing a previously uploaded file in storage (used if Embedding Source is File Store Key).
File Content Direct content of a file to generate embeddings for (can be used alongside other inputs).
Type The type of content being processed. Options: Audio, Image, PDF, Text, Text Other.
Token Overflow Mode Behavior when input exceeds token limits: Error to throw an error, or Truncate to cut off excess tokens.

Output

The node outputs JSON data containing the generated embeddings. Typically, this will be an array or object representing the vector embedding(s) corresponding to the input content. This output can then be used downstream for similarity comparisons, indexing, or further processing.

If binary data is involved (e.g., image or audio embeddings), the node may handle or reference such data internally, but the main output focus is on the embedding vectors in JSON form.

Dependencies

  • Requires an API key credential for authenticating with the JigsawStack API service.
  • The node sends requests to the JigsawStack API endpoint at https://api.jigsawstack.com/v1.
  • Proper configuration of the API key credential in n8n is necessary for successful operation.

Troubleshooting

  • Input Exceeds Token Limits: If the input content is too large, and Token Overflow Mode is set to Error, the node will throw an error. To resolve, either reduce input size or switch the mode to Truncate to automatically shorten the input.
  • Invalid or Missing API Key: Authentication errors occur if the API key is missing or invalid. Ensure the API key credential is correctly configured in n8n.
  • Unsupported Content Type: Providing a content type not supported by the API or mismatched with the actual content may cause failures. Verify the Type property matches the input content.
  • Incorrect Embedding Source: If the selected embedding source does not correspond to provided input (e.g., selecting URL but not providing a URL), the node may fail or produce empty results. Double-check input fields based on the chosen source.

Links and References

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